Bharat in Betastartup field guide
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No. 1246Problem statementBuilt environment

Automated lntegration and lntelligent Harmonization of Multi-source Geospatial Data for urban Land Record Management.

Source

Source summary

The requested AI-enabled platform should harmonize urban land datasets, including drone imagery, elevation models, cadastral and revenue records, municipal and utility layers, field verification and GNSS/CORS surveys. Spatial matching, attribute mapping, coordinate transformation, topology correction, change detection, conflict handling and confidence scores should reconcile these with AI-extracted cadastral features. The aim is interoperable records rather than separate manual GIS integrations.

No. 564Problem statementBuilt environment

Al based system to develop a Digital Elevation Model of the areas to work out various tunnel alignments.

The challenge asks for an AI-assisted digital elevation model that compares possible tunnel alignments using terrain gradient, approach roads to tunnel portals, construction cost, time and other factors. Users should be able to toggle the factors that apply to a particular comparison.

No. 1170Problem statementBuilt environment

Platform for Inter-departmental cooperation (at city level) in Indian Cities, for sharing data & resources, unified phasing, planning and implementation of projects.

Create a city-level interdepartmental coordination platform registering agencies and personnel, sharing geolocated projects and available expertise/equipment, and managing tasks, schedules, reports and responsibilities. Identify projects sharing a site so agencies can phase work together, avoiding repeated disruption such as road reconstruction followed by utility installation. Include meeting/capacity-building support and separate internal, interagency and public discussion areas with appropriate access.

No. 1171Problem statementBuilt environment

Utilization of images for monitoring of progress of construction activities for building construction projects.

Develop ML-based progress monitoring specifically for building construction. Users upload site images and identify building count and activity, such as foundations, superstructure, façades or interiors. Identify the stage, compare previous images and flag mismatches between selected activities and imagery for correction. The source envisages different trained models for components, supporting government agencies that cannot visit every site frequently.